Study on Collision Avoidance Strategies Based on Social Force Model Considering Stochastic Motion of Pedestrians in Mixed Traffic Scenario

Abstract

In typical traffic scenarios where there are no clear separations between the traffic participants, such as mixed traffic or shared space, vehicles and pedestrians are usually moving in the same time so that ego vehicle may need to face with multiple pedestrians in a relatively short interaction distance. Considering the stochastic motion of pedestrians and to balance the time consumption and safety during passing process, this paper proposes two strategies of collision avoidance (CA) for ego vehicle, which are based on model predictive control (MPC) and social force model (SFM). Besides, a modified SFM-based pedestrian model that considers the stochastic motion is given to evaluate the effectiveness of the proposed strategies. For MPC-based CA strategy, considering the unpredictable motion of the pedestrians, a novel speed re-planning layer combined with collision probability estimation, which is used to calculate an acceptable maximum safe speed for ego vehicle, is proposed. On the other hand, parameters associated with the SFM-based vehicle model are re-calibrated by particle swarm optimization (PSO) and the calibration process has been analyzed physically in details. The recommended values based on different initial interaction speed and distance of vehicle and pedestrians are also determined for further reference as useful findings from the analysis.